EM algorithms for estimating the Bernstein copula
EM algorithms for estimating the Bernstein copula
复制标题
DOI：
10.1016/j.csda.2014.01.009
复制
发表时间：
2016-01-01
影响因子：
1.8
通讯作者：
Richards, Donald
中科院分区：
文献类型：
--
作者：
Dou, Xiaoling;Kuriki, Satoshi;Richards, Donald
提出了一种利用有限混合分布的Bernstein copula表示，利用序统计量构造具有固定边际的多元分布的方法。提出了Bernstein copula估计的期望最大化算法，并证明了该算法的局部收敛性。此外，给出了所提半参数估计量的渐近性质。使用三个真实数据集和一个三维模拟数据集给出了说明性示例。这些研究表明，Bernstein copula能够灵活地表示各种分布，并且所提出的EM算法可以很好地处理这些数据。(C) 2014 Elsevier B.V.版权所有
A method that uses order statistics to construct multivariate distributions with fixed marginals and which utilizes a representation of the Bernstein copula in terms of a finite mixture distribution is proposed. Expectation maximization (EM) algorithms to estimate the Bernstein copula are proposed, and a local convergence property is proved. Moreover, asymptotic properties of the proposed semiparametric estimators are provided. Illustrative examples are presented using three real data sets and a 3-dimensional simulated data set. These studies show that the Bernstein copula is able to represent various distributions flexibly and that the proposed EM algorithms work well for such data. (C) 2014 Elsevier B.V. All rights reserved.